Detection and estimation of gas hydrates using rock physics and seismic inversion: Examples from the northern deepwater Gulf of Mexico

2004 ◽  
Vol 23 (1) ◽  
pp. 60-66 ◽  
Author(s):  
Jianchun Dai ◽  
Haibin Xu ◽  
Fred Snyder ◽  
Nader Dutta
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrates ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Detection And Estimation

Quantitative interpretation for gas hydrate accumulation in the eastern Green Canyon Area, Gulf of Mexico using seismic inversion and rock physics transform

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. B139-B150 ◽  
Author(s):  
Zijian Zhang ◽  
De-hua Han ◽  
Qiuliang Yao
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Pore Space ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Bright Spot ◽  
Quantitative Interpretation ◽  
Free Gas ◽  
Qualitative And Quantitative ◽  
Amplitude Data

Gas hydrate can be interpreted from seismic data through observation of bottom simulating reflector (BSR). It is a challenge to interpret gas hydrate without BSR. Three-dimensional qualitative and quantitative seismic interpretations were used to characterize gas hydrate distribution and concentration in the eastern Green Canyon area of the Gulf of Mexico, where BSR is absent. The combination of qualitative and quantitative interpretation reduces ambiguities in the estimation and identification of gas hydrate. Sandy deposition and faults are qualitatively interpreted from amplitude data. The 3D acoustic impedance volume was interpreted in terms of high P-impedance hydrate zones and low P-impedance free gas zones. Gas hydrate saturation derived from P-impedance is estimated by a rock physics transform. We interpreted gas hydrate in the sand-prone sediments with a maximum saturation of approximately 50% of the pore space. Sheet-like and some bright spot gas hydrate accumulations are interpreted. The interpretation of sheet-like gas hydrate within sand-prone sediments around faults suggests that fluid moves into the sand zones laterally by conduits. Variations in depths of interpreted gas hydrate zones imply nonequilibrium conditions. Low P-impedance free gas zones within high P-impedance gas hydrate zones imply possible coexistence of hydrate and free gas within the hydrate stability zone. We propose that gas hydrate distribution and concentration are associated with structures, buried sedimentary bodies, sources of gas, and fluid flux.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

Rock physics model‐based seismic inversion

2006 ◽  
Author(s):  
Kyle Spikes ◽  
Jack Dvorkin ◽  
Gary Mavko
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Model Based ◽  
Physics Model ◽  
Rock Physics Model

Sea bottom characteristics and geochemistry of oil and gas seeps in the Gulf of Mexico

2021 ◽  
pp. 1-60
Author(s):  
John Decker ◽  
Philip Teas ◽  
Daniel Orange ◽  
Bernie B. Bernard
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrates ◽  
Oil And Gas ◽  
Geochemical Analysis ◽  
Mud Volcanoes ◽  
Sea Bottom ◽  
High Backscatter ◽  
Oil Extract ◽  
Brine Pools ◽  
Gas Seeps

From 2015 to 2018, TGS conducted a comprehensive multiclient oil and gas seep hunting survey in the Gulf of Mexico. The basis for identifying seeps on the sea bottom was a high-resolution Multi-Beam Echo Sounder survey, mapping approximately 880,000 km2 of the sea bottom deeper than 750 m water depth, at a bathymetric resolution of 15 m and a backscatter resolution of 5 m. We have identified more than 5000 potential oil and/or gas seeps, and of those, we cored approximately 1500 for hydrocarbon geochemical analysis. The sea bottom features best related to hydrocarbon seepage in the GoM are high backscatter circular features with or without bathymetric expression, high backscatter features with “flow” appearance, mud volcanoes, pock marks, brine pools, “popcorn” texture, faults, and anticlinal crests. We also tracked gas plumes in the water column back to the sea bottom to provide an additional criterion for hydrocarbon seepage. Cores from sea bottom targets recovered liquid oil, tar, and gas hydrates. Oil extract and gas analyses of samples from most target types produced values substantially higher than background in oil and gas.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

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